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feat(models): Add GAN to Modelhub (#5)
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--- | ||
template: hub1 | ||
title: Generative Adversarial Networks | ||
summary: | ||
en_US: GAN pre-trained on Cifar10 | ||
zh_CN: 生产对抗网络(Cifar10 预训练权重) | ||
author: MegEngine Team | ||
tags: [vision, gan] | ||
github-link: https://github.com/MegEngine/Models/tree/master/official/vision/gan | ||
--- | ||
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<!-- section: zh_CN --> | ||
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#### 训练参数 | ||
| 分辨率 | 批大小 | 学习率 | β<sub>1</sub> | β<sub>2</sub> | 衰减法则 | n<sub>dis</sub> | n<sub>iter</sub> | | ||
|:----------:|:----------:|:-------------:|:-------------:|:-------------:|:------------:|:---------------:|------------------| | ||
| 32 x 32 | 64 | 2e-4 | 0.0 | 0.9 | Linear | 5 | 100K | | ||
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#### 评测指标 | ||
| Metric | Method | | ||
|:--------------------------------:|:---------------------------------------:| | ||
| [Inception Score (IS)](https://arxiv.org/abs/1606.03498) | 分成10份共计 50K 样本 | | ||
| [Fréchet Inception Distance (FID)](https://arxiv.org/abs/1706.08500) | 50K 真实/生成样本 | | ||
| [Kernel Inception Distance (KID)](https://arxiv.org/abs/1801.01401) | 50K 真实/生成样本, 分成10份取平均值| | ||
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#### Cifar10 结果 | ||
| 模型 | FID Score | IS Score | KID Score | | ||
| :-: | :-: | :-: | :-: | | ||
| DCGAN | 27.2 | 7.0 | 0.0242 | | ||
| WGAN-WC | 30.5 | 6.7 | 0.0249 | | ||
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### 参考文献 | ||
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- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434), Alec Radford, Luke Metz, and Soumith Chintala. | ||
- [Wasserstein GAN](https://arxiv.org/abs/1701.07875), Martin Arjovsky, Soumith Chintala, and Léon Bottou. | ||
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<!-- section: en_US --> | ||
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#### Training Parameters | ||
| Resolution | Batch Size | Learning Rate | β<sub>1</sub> | β<sub>2</sub> | Decay Policy | n<sub>dis</sub> | n<sub>iter</sub> | | ||
|:----------:|:----------:|:-------------:|:-------------:|:-------------:|:------------:|:---------------:|------------------| | ||
| 32 x 32 | 64 | 2e-4 | 0.0 | 0.9 | Linear | 5 | 100K | | ||
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#### Metrics | ||
| Metric | Method | | ||
|:--------------------------------:|:---------------------------------------:| | ||
| [Inception Score (IS)](https://arxiv.org/abs/1606.03498) | 50K samples at 10 splits| | ||
| [Fréchet Inception Distance (FID)](https://arxiv.org/abs/1706.08500) | 50K real/generated samples | | ||
| [Kernel Inception Distance (KID)](https://arxiv.org/abs/1801.01401) | 50K real/generated samples, averaged over 10 splits.| | ||
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#### Cifar10 Results | ||
| Method | FID Score | IS Score | KID Score | | ||
| :-: | :-: | :-: | :-: | | ||
| DCGAN | 27.2 | 7.0 | 0.0242 | | ||
| WGAN-WC | 30.5 | 6.7 | 0.0249 | | ||
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### References | ||
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- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434), Alec Radford, Luke Metz, and Soumith Chintala. | ||
- [Wasserstein GAN](https://arxiv.org/abs/1701.07875), Martin Arjovsky, Soumith Chintala, and Léon Bottou. |